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"description": "robust-ml\n=========\n\nInterfaces for defining Robust ML models and precisely specifying the threat\nmodels under which they claim to be secure. Also includes interfaces for\nspecifying attacks and evaluating attacks against models.\n\nThe motivation behind this project is to make it easy to make specific,\ntestable claims about the robustness about machine learning models. Read more\nin the `FAQ `__.\n\nInstallation\n------------\n\nYou can install from PyPI: ``pip install robustml``.\n\nUsage\n-----\n\nSee `this repository `__ for a complete\nexample of implenenting a model, implementing an attack, and evaluating the\nattack against the model.\n\nIf you're implementing a **defense**, you should implement\n``robustml.model.Model``. See `here\n`__ for an\nexample.\n\nIf you're implementing an **attack** against a specific defense, you should\nimplement ``robustml.attack.Attack``. See `here\n`__ for an example.\n\nTo **evaluate** a specific attack against a specific defense, use\n``robustml.evaluate.evaluate()``. See `here\n`__ for an example.\n\nContributing\n------------\n\nDo you have ideas on how to improve the robustml package? Have a feature\nrequest (such as a specification of a new threat model) or bug report? Great!\nPlease open an `issue `__ or\nsubmit a `pull request `__.\n\nBefore contributing a major change, it's recommended that you open a pull\nrequest first and get feedback on the idea before investing time in the\nimplementation.\n\nPackaging\n---------\n\n1. Update version information.\n\n2. Build the package using ``python setup.py sdist bdist_wheel``.\n\n3. Sign and upload the package using ``twine upload -s dist/*``.\n\n4. Create a signed tag in the git repo with the version number that was\n uploaded to PyPI.\n\n\n",
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